[unreadable] The Cancer Genome Atlas (TCGA) is a comprehensive and coordinated effort to accelerate our understanding of the molecular basis of cancer through the application of genome analysis technologies. Computational biology is rising to the challenge of analyzing these rich datasets by combining advanced technology with prior biological knowledge. Computational tools will be designed and implemented to make sense of the integrated whole of Cancer Genome Atlas datasets, including somatic mutations, amplification and deletion of genes and gene segments, expression of messenger RNAs and microRNAs, epigenetic DNA methylation and, in the future, protein expression and modification. Specifically, the project plans to map available data types onto cell network nodes, such as nodes for genes and proteins; and, to create visualization methods to simultaneously or in time sequence represent the molecular alterations observed in specific tumors. Browsing this rich information will be supported by pathway information from publicly available pathway databases, made available through the Pathway Commons portal using the bioPAX pathway exchange language. Cancer researchers will use these tools to visually analyze the role of alterations not only of individual genes and gene sets, but also of functional modules consisting of connected regions in pathway space, indicative of activated or deactivated oncogenic processes. Complementing visual analysis, computational algorithms will be developed to delineate active processes or modules by neighborhood analysis; and, to discover tumor subtypes and subtype-specific molecular events (markers) using two methods, combinatorial entropy optimization and correspondence analysis. The potential results will be the discovery of processes that drive tumor development, the identification of drug targets for specific cancers and a more detailed understanding of oncogenesis. In collaboration with wet lab biologists and clinical researchers, this computational biology project aims to make substantial improvements to the prevention, early detection and effective therapy of cancer. [unreadable] [unreadable] [unreadable]